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fter a period of rapid growth, company managers may step back and ask if their current facilities structure — often haphazardly cobbled together through mergers and acquisitions and building on existing operations — can be rationalized in a strategic manner to more efficiently and cost-effectively meet the needs of the integrated firm.
Moreover, real estate and other decisionmakers increasingly realize that traditional location decision criteria (facility availability and cost per square foot) could be confounded by other factors, such as labor costs and quality of life, that may have long-term impacts on the bottom line long after the initial location decision is made.
These were questions raised by real estate decisionmakers and advisors at a major U.S.-based financial services firm. After growing substantially in the 1980s and ’90s, in part through a series of acquisitions, the company sought to take a more strategic approach to location decisions. They asked The Rand Corporation to assist them with two projects to guide their location assessment and decision making.
New Questions for Location Managers
The company’s managers were planning to invest substantially in major new back-office facilities to support continued expansion of their business lines and operations. At the same time, they wanted to consider a range of factors beyond traditional real estate cost and availability criteria. This more strategic view was driven in part by several factors.
Tight labor markets in the 1990s (especially in regions where the client already had a large back-office presence) and predictions that conditions would not improve in the foreseeable future led company managers to wonder if they soon would find themselves having to compete more aggressively in some of their traditional labor markets just as the company was seeking to hire hundreds of new employees.
A key enterprise-wide priority was to make use of new information technologies to facilitate electronic service delivery, which would reduce the importance of having regionally based staffs. This enabled real estate managers to consider a wider range of locations for future operations.
As the company was seeking to develop its business in new regions, some managers wondered whether building up an operations presence — “planting the company flag” — would help generate brand recognition.
Finally, given tight labor markets nationwide, managers realized that such quality-of-life factors as schools, traffic and recreational opportunities were becoming increasingly important in terms of staff acquisition, retention and productivity. These factors are especially important to IT personnel and younger staff on whom the company relies heavily. Anecdotally, real estate and business managers suspected that community infrastructure and quality-of-life factors varied across the firm’s existing locations, but they had no experience compiling or analyzing such information, let alone applying them to business decisions.
To address these concerns, Rand developed a community indicators database and interactive ranking tool tailored to client priorities to help upper management select the best sites overall.
Finding the Data
The client asked Rand to measure and evaluate a broad range of community attributes, such as work force age and education, economic profile, housing costs, transportation, education and community amenities for over 24 locations across the United States.
To start, Rand held focused discussions with key managers in the firm’s business, real estate, human resources and technical-support units to draw out the decision criteria they considered important. Of primary concern was ascertaining which factors had the greatest impact on the firm’s ability to attract and retained highly trained personnel. In addition to labor market issues, such as age group size, presence of competing employers, and educational attainment, other quality-of-life considerations such as weather, schools and housing were identified. The client’s real estate department had sufficient knowledge of property costs and availability so that these indicators were not considered after the first phase of analysis.
With these views in hand, we identified the best data available to measure each indicator, drawing from previous experience working on labor, economic, livability and other issues for clients such as the Social Security Administration and the U.S. Environmental Protection Agency. To properly evaluate and compare locations, it is crucial that each factor be consistently measured in terms of time period and geographic area and be available for a large sample of sites. Therefore, factors were dropped from the list if high-quality, consistent measures covering locations across the U.S. were unavailable. Availability of quality daycare, for example, is one factor that is not measured adequately nationwide.
The final database consisted of 64 separate community indicators standardized for each of the 24 locations.
Establishing Client Priorities
Next, Rand created an electronic survey instrument, which we administered to approximately 50 senior and mid-management personnel throughout the company, asking them to rate the importance they assigned to each factor. We standardized the respondents’ ratings and derived weights for each factor.
Overall, respondents assigned the most importance to the educational attainment factor. Based on the survey results, the education attainment indicator received 12 times the weight of the age distribution measure, the least important. Another factor ranked as important by company respondents was the local economy (i.e. percent jobs in financial services and computer-science fields, and the unemployment rate). Less important were transportation issues, such as use of carpools and public transportation. Interestingly, human resource managers that needed to fill positions requiring relatively limited education and experience reported the same location priorities as recruiters for highly trained and experienced IT personnel.
Next, we multiplied the weights for each factor, multiplied them by each city’s score for the factor, and summed these products to derive a city-specific total ranking score. (Rankings for some sample locations are shown in the table). With this ranking process, Rand was able to produce robust results that grouped the client’s candidate locations into three broad categories: top tier, bottom tier and those in between.
Sharing Rating Information
Throughout the Firm
The client wanted to share the location analysis tool with key decision makers and avoid a static paper report that would sit in one office. In response, Rand created an online, interactive “Location Selector” that allows users to compare and rank locations according to any set of factors. For example, users can also view how locations ranked according to a single indicator and view the complete profile of indicators for a single location.
One may surmise that moving to an area with higher unemployment and a larger percentage of population currently employed in similar positions would provide the best opportunities for hiring entry-level personnel. Accordingly, a Web site user can select these criteria to compare and rank locations according to company manager preferences.
Once into the project, the client requested that more cities be included because of ongoing acquisitions. The tool therefore was designed to be easily updateable and scalable so that additional cities or evaluation criteria could be added with relative ease.
The firm is currently using the tool for three purposes: (1) to evaluate which locations to consolidate; (2) to evaluate which sites to retain in the event of future acquisitions; and (3) to forecast which locations would be most suitable to align specific practice groups, such as IT support.
When they first conceived of the project, the clients had in mind a large number of location criteria to integrate and evaluate. The approach we used helped them focus on the most salient issues.
Although the performance of existing operations varied across the different criteria, firm managers learned that at their existing locations, no one factor was a “show-stopper” that would motivate them to immediately reconsider maintaining operations there. Rather, the analysis raised flags to keep in mind over the longer term.
Expectations about which cities would fare the best and which worst were not always met. For instance, Omaha, Nebraska (a smaller and stable labor market with historically low unemployment), ranked first according to the clients’ location preferences, while Los Angeles (a large and fast-growing labor market) ranked last.
Most importantly, the tool freed the clients to focus on the most critical issues — establishing their priorities and making decisions — rather than trying to scramble for information and figuring out the best way to weigh disparate issues on the fly.
